SYSTEMS AND METHODS FOR DATA CLEANSING SUCH AS FOR OPTIMIZING CLINICAL SCHEDULING

• US 20170212994A1
• Filed: 04/07/2017
• Published: 07/27/2017
• Est. Priority Date: 12/17/2015
• Status: Active Grant

• Current Assignee: Kairoi Healthcare Strategies, Inc.
• Original Assignee: Kairoi Healthcare Strategies, Inc.
• Inventors: Lee, Russell D., Wu, Jeffrey
• Granted Patent: US 10,204,705 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A scheduling system and method for data cleansing may be used to optimize clinical scheduling. The present disclosure describes receiving clinical record data, in an agnostic manner, from a system including a source scheduling database containing the clinical record data; mapping the clinical record data to a desired format; conforming the clinical record data to standardized scheduling elements of the scheduling system; cleansing, in a manner configurable by a user, the clinical record data to purge portions of the clinical record data; providing the clinical record data to an optimization engine for optimization of the clinical record data; optimizing the clinical record data by applying configurable logic to the clinical record data; and uploading one or more newly defined optimized scheduling templates via an outbound connection back to the scheduling system.

Claims

1. A scheduling system configured for data cleansing to optimize clinical scheduling, the system comprising:

one or more hardware processors configured by machine-readable instructions to;

receive clinical record data, in an agnostic manner, from a system including a source scheduling database containing the clinical record data;

map the clinical record data to a desired format, the desired format including a plurality of fields;

conform the clinical record data to standardized scheduling elements of the scheduling system by parsing the clinical record data, the parsing including reformatting the clinical record data by assigning portions of the data to appropriate fields;

cleanse, in a manner configurable by a user, the clinical record data to purge portions of the clinical record data related to one or more of data errors, data artifacts, or business logic;

provide the clinical record data to an optimization engine for optimization of the clinical record data;

optimize the clinical record data by applying configurable logic to the clinical record data in order to provide one or more newly defined optimized scheduling templates that configure one or both of providers and rooms for optimal usage of time, where provider availability is matched with customized variables, the customized variables including one or more of visit complexity, average visit length, number of exam rooms, provider preference, or non-physician resources; and

upload one or more newly defined optimized scheduling templates via an outbound connection back to the scheduling system.

2. The system of claim 1, wherein conforming the clinical record data allows the scheduling system to work with any electronic medical record.

3. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to allow a primary fact table of data to be surrounded by dimensional tables, the primary fact table of data including standardized scheduling elements that include one or more of appointment information, subject and provider keys, appointment scheduling start times, or appointment scheduling end times, the dimensional tables including one or more of standardized dimensional tables, subject dimensional tables, or provider dimensional tables.

4. The system of claim 3, wherein the standardized dimensional tables include detail information that includes one or more of appointment outcome information having appointment statuses, actual appointment start times, or actual appointment end times, and wherein subject and provider dimensional tables include detail information that includes one or both of subject demographics or provider specialty information.

5. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to utilize a star schema extensible framework, the star schema extensible framework including one or more fact tables referencing any number of dimension tables, the fact tables including one or both of numerical values or information regarding where descriptive information is kept, or the dimension tables including records with attributes to describe the fact data.

6. The system of claim 1, wherein the scheduling system includes subject and provider dimensional tables with details that include one or both of subject demographics or provider specialty information.

7. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to extract data from the source scheduling database via one or more of an OLEDB connection, an ODBC connection, or an API.

8. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to run one or more cleansing functions to ignore one or more of data that is incomplete, appointment information that is missing a start time, or appointment information that is missing an end time.

9. The system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to cleanse data errors including rows of data missing information.

10. The system of claim 1, wherein data that is excluded from the optimization engine is stored for analytics and reporting, allowing insight into types and amounts of data that are being excluded from analysis, the insight into types and amounts of data that are being excluded from analysis including one or more of data with missing information, data that is not relevant, data that is incomplete, or data that is erroneous.

11. A scheduling method configured for data cleansing to optimize clinical scheduling, the method being performed by one or more hardware processors configured by machine-readable instructions, the method comprising:

receiving clinical record data, in an agnostic manner, from a system including a source scheduling database containing the clinical record data;

mapping the clinical record data to a desired format, the desired format including a plurality of fields;

conforming the clinical record data to standardized scheduling elements of the scheduling system by parsing the clinical record data, the parsing including reformatting the clinical record data by assigning portions of the data to appropriate fields;

cleansing, in a manner configurable by a user, the clinical record data to purge portions of the clinical record data related to one or more of data errors, data artifacts, or business logic;

providing the clinical record data to an optimization engine for optimization of the clinical record data;

optimizing the clinical record data by applying configurable logic to the clinical record data in order to provide one or more newly defined optimized scheduling templates that configure one or both of providers and rooms for optimal usage of time, where provider availability is matched with customized variables, the customized variables including one or more of visit complexity, average visit length, number of exam rooms, provider preference, or non-physician resources; and

uploading one or more newly defined optimized scheduling templates via an outbound connection back to the scheduling system.

12. The method of claim 11, wherein conforming the clinical record data allows the scheduling system to work with any electronic medical record.

13. The method of claim 11, wherein the one or more hardware processors are further configured by machine-readable instructions to allow a primary fact table of data to be surrounded by dimensional tables, the primary fact table of data including standardized scheduling elements that include one or more of appointment information, subject and provider keys, appointment scheduling start times, or appointment scheduling end times, the dimensional tables including one or more of standardized dimensional tables, subject dimensional tables, or provider dimensional tables.

14. The method of claim 13, wherein the standardized dimensional tables include detail information that includes one or more of appointment outcome information having appointment statuses, actual appointment start times, or actual appointment end times, and wherein subject and provider dimensional tables include detail information that includes one or both of subject demographics or provider specialty information.

15. The method of claim 11, wherein the one or more hardware processors are further configured by machine-readable instructions to utilize a star schema extensible framework, the star schema extensible framework including one or more fact tables referencing any number of dimension tables, the fact tables including one or both of numerical values or information regarding where descriptive information is kept, or the dimension tables including records with attributes to describe the fact data.

16. The method of claim 11, wherein the scheduling system includes subject and provider dimensional tables with details that include one or both of subject demographics or provider specialty information.

17. The method of claim 11, wherein the one or more hardware processors are further configured by machine-readable instructions to extract data from the source scheduling database via one or more of an OLEDB connection, an ODBC connection, or an API.

18. The method of claim 11, wherein the one or more hardware processors are further configured by machine-readable instructions to run one or more cleansing functions to ignore one or more of data that is incomplete, appointment information that is missing a start time, or appointment information that is missing an end time.

19. The method of claim 11, wherein the one or more hardware processors are further configured by machine-readable instructions to cleanse data errors including rows of data missing information.

20. The method of claim 11, wherein data that is excluded from the optimization engine is stored for analytics and reporting, allowing insight into types and amounts of data that are being excluded from analysis, the insight into types and amounts of data that are being excluded from analysis including one or more of data with missing information, data that is not relevant, data that is incomplete, or data that is erroneous.